{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MICRO 2022 Artifact Evaluation\n",
    "# Sparseloop: An Analytical Approach To Sparse Tensor Accelerator Modeling\n",
    "### Yannan N. Wu, Po-An Tsai, Angshuman Parashar, Vivienne Sze, Joel S. Emer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Context\n",
    "\n",
    "In this artifact, we provide evaluation setups for the important experiments (the motivating example, validations for accelerator designs, and the case study). Please follow the instructions in each cell to run the evaluations.\n",
    "\n",
    "For each experiment, we give a *very conservative estimate* of how long the runs will take. The input specifications and related scripts can be found in `../evaluation_setups`. **The easiest way to validate the outputs is to compare the generated figure/table to the figure/table in the paper**, but we do provide a `ref_outputs` folder for each evaluation for more detailed comparison of results if necessary.\n",
    "\n",
    "\n",
    "\n",
    "***Please note that to allow easier and faster reproduction, for most of the experiments, we directly provide the mappings found by our offline search*** *instead of letting Sparseloop performing online seach for the best mapping.* ***Sparseloop does allow automatic exploration of various mappings*** *as demonstrated in eval1 (more information on tool usage can be found [here](https://accelergy.mit.edu/tutorial.html) and [here](https://accelergy.mit.edu/sparse_tutorial.html)).*\n",
    "\n",
    "\n",
    "### If you have any questions, please feel free to reach out at timeloop-accelergy@mit.edu. Thanks!\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Eval 1: Data representation format comparison (Fig.1 in paper)\n",
    "\n",
    "This evaluation compares the processing speed and energy efficiency of two architectures that support different representation formats: 1) bitmask with gating; 2) coordinate list with skipping, running matrix multiplication workloads with various density degrees\n",
    "\n",
    "### Step 1: Run sweep  ( <5min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%%bash\n",
    "cd ../evaluation_setups/fig1_format_comparison_setup/scripts\n",
    "echo \"input specs at: ../evaluation_setups/fig1_format_comparison_setup/\"\n",
    "chmod +x sweep.sh\n",
    "time ./sweep.sh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Parse and generate plot\n",
    "1) run first cell to parse and plot\n",
    "\n",
    "2) run next cell to display the generated figures\n",
    "\n",
    "3) compare the figures to Fig.1 in paper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "cd ../evaluation_setups/fig1_format_comparison_setup/scripts\n",
    "time python3 parse_and_plot.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Video, Image, HTML, display\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "cycles_fig = \"../evaluation_setups/fig1_format_comparison_setup/outputs/fig_cycles.png\"\n",
    "energy_fig = \"../evaluation_setups/fig1_format_comparison_setup/outputs/fig_energy_pJ.png\"\n",
    "\n",
    "display(Image(cycles_fig))\n",
    "display(Image(energy_fig))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Eval 2: EyerissV2 PE Validation ( 1. Fig.12 in paper; 2. showcase the main idea presented in Table 5)\n",
    "\n",
    "We simulate mobilenet0.5 layers on a variation of eyeriss v2 PE with one compute unit.\n",
    "\n",
    "We show experimental setups for both running evaluations with statistical density model (uniform density model) and actual data based density model. The modeling time is much longer with actual data based model. **This fact showcases the idea presented in Table 5: analytical modeling with statisical density model allows faster modeling.** (*Experiments for Table 5 requires manaul instrumantaion into the source code, which is not convienent for non-developers to setup, so we use this evlauation to showcase the main idea.*)\n",
    "\n",
    "### Step 1: Run sweep with statistical density modeling (< 3min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%%bash\n",
    "cd ../evaluation_setups/fig12_eyerissv2_pe_setup/scripts\n",
    "echo \"input specs at: ../evaluation_setups/fig12_eyerissv2_pe_setup/\"\n",
    "time python3 sweep.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Parse and generte plot\n",
    "\n",
    "1) run first cell to parse and generate figure\n",
    "\n",
    "2) run next cell to diplay the generated figure\n",
    "\n",
    "3) compare to per layer the ground truth and uniform modeling bars in Fig.12 in the paper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "cd ../evaluation_setups/fig12_eyerissv2_pe_setup/scripts\n",
    "python3 parse_and_plot.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Video, Image, HTML, display\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = \"../evaluation_setups/fig12_eyerissv2_pe_setup/outputs/uniform_only/fig.png\"\n",
    "\n",
    "display(Image(fig))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Run autal data evaluation on three layers ( <30 min)\n",
    "\n",
    "Since actual data based evaluations are slow compared to statistical ones, the default evaluation only run on three layers. Please tune the `--max_layers` knob in the command below if you would like to try more layers.\n",
    "\n",
    "**Note that this also demonstrate that our modeling speed with the statiscal density models are fast, which is the main idea presented in Table 5 in paper**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "%%bash\n",
    "cd ../evaluation_setups/fig12_eyerissv2_pe_setup/scripts\n",
    "echo \"input specs at: ../evaluation_setups/fig12_eyerissv2_pe_setup/\"\n",
    "\n",
    "# update the number of max layers below (<=8) to see more bar groups in fig12, but the run will take longer\n",
    "time python3 sweep.py --include_actual --max_layers 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4: Parse and generate plot\n",
    "\n",
    "\n",
    "1) run first cell to parse and generate figure\n",
    "\n",
    "2) run next cell to diplay the generated figure\n",
    "\n",
    "3) compare to the group of bars for **L07** in Fig.12 in the paper. \n",
    "   The main idea here is that compared to `baseline`, `acutal_data` based evlauation is able to accurately capture design behaviors with no error, whereas there is some error due statistical approximations when using the `uniform` distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "cd ../evaluation_setups/fig12_eyerissv2_pe_setup/scripts\n",
    "python3 parse_and_plot.py --include_actual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Video, Image, HTML, display\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = \"../evaluation_setups/fig12_eyerissv2_pe_setup/outputs/uniform_actual/fig.png\"\n",
    "\n",
    "display(Image(fig))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Eval 3: Eyeriss Validation (Table 7 in paper)\n",
    "\n",
    "We simulate the AlexNet Conv layers on the Eyeriss accelerator, and present the DRAM traffic compression ratio. \n",
    "\n",
    "\n",
    "### Step 1: Run sweep (< 5min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%%bash\n",
    "cd ../evaluation_setups/table7_eyeriss_setup/scripts\n",
    "echo \"input specs at: ../evaluation_setups/table7_eyeriss_setup/\"\n",
    "time python3 sweep.py \n",
    "# To enable mapspace search, uncomment command below (note that the run will take much longer)\n",
    "# mapspace search might end up with a slightly different mapping due to runtime randomness\n",
    "# time python3 sweep.py --search_mapping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Parse and generate table\n",
    "\n",
    "1) run the first cell to parse results and print table entries\n",
    "\n",
    "2) compare the entries to the second row in table 7 in the paper\n",
    "\n",
    "***Please note that we used the public 45nm data for energy modeling in this evaluation instead of the 65nm private PDK used in our paper's evaluations.*** *Although the the DRAM compression ratio is not impacted, the energy reported in the raw results can be different. However, we do see significant energy reduction with the gating SAF implemented in this architecture.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "cd ../evaluation_setups/table7_eyeriss_setup/scripts\n",
    "python3 parse_and_plot.py\n",
    "\n",
    "# If used mapspace search in the previous step\n",
    "# python3 parse_and_plot.py --stats_prefix timeloop-mapper"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Eval 4: Dual Side Sparse Tensor Core Validation (Fig.13 in paper)\n",
    "\n",
    "We simulate matrix multiplication workloads with various sparsity degrees running on dual side sparse tensor core design.\n",
    "\n",
    "### Step 1: Run sweep ( < 5min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "cd ../evaluation_setups/fig13_dstc_setup/scripts\n",
    "echo \"input specs at: ../evaluation_setups/fig13_dstc_setup/\"\n",
    "time python3 sweep.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Parse and generate plot\n",
    "\n",
    "1) run first cell to parse and generate figure\n",
    "\n",
    "2) run next cell to diplay the generated figure\n",
    "\n",
    "3) compare to Fig.13 in the paper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "cd ../evaluation_setups/fig13_dstc_setup/scripts\n",
    "python3 parse_and_plot.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Video, Image, HTML, display\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = \"../evaluation_setups/fig13_dstc_setup/outputs/fig.png\"\n",
    "\n",
    "display(Image(fig))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Eval 5: Sparse tensor core validation and the related case study (Figure 15 in paper)\n",
    "\n",
    "We simulate the selected ResNet50 layers on the sparse tensor core (STC) accelerator and compare various sparse designs based on the tensor core setup. \n",
    "\n",
    "Please see more details on the various designs validated/explored in Section 6.3.5 and Section 6.4 in the paper. \n",
    "\n",
    "\n",
    "### Step 1: Run sweep ( < 8min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input specs at: ../evaluation_setups/fig15_stc_related_setup/\n",
      "Utilization = 1.00 | pJ/Compute =    1.832\n",
      "Utilization = 1.00 | pJ/Algorithmic-Compute =    1.643 | pJ/Compute =    1.643\n",
      "Utilization = 0.83 | pJ/Algorithmic-Compute =    2.415 | pJ/Compute =    5.489\n",
      "Utilization = 1.00 | pJ/Algorithmic-Compute =    1.643 | pJ/Compute =    1.643\n",
      "Utilization = 1.00 | pJ/Algorithmic-Compute =    1.643 | pJ/Compute =    1.643\n",
      "Utilization = 1.00 | pJ/Algorithmic-Compute =    1.311 | pJ/Compute =    1.311\n",
      "Utilization = 1.00 | pJ/Compute =    1.845\n",
      "Utilization = 1.00 | pJ/Algorithmic-Compute =    1.682 | pJ/Compute =    1.682\n",
      "Utilization = 0.72 | pJ/Algorithmic-Compute =    1.502 | pJ/Compute =    5.564\n",
      "Utilization = 1.00 | pJ/Algorithmic-Compute =    1.682 | pJ/Compute =    1.682\n",
      "Utilization = 1.00 | pJ/Algorithmic-Compute =    1.682 | pJ/Compute =    1.682\n",
      "Utilization = 1.00 | pJ/Algorithmic-Compute =    1.271 | pJ/Compute =    1.271\n",
      "Utilization = 1.00 | pJ/Compute =    1.781\n",
      "Utilization = 1.00 | pJ/Algorithmic-Compute =    1.649 | pJ/Compute =    1.649\n",
      "Utilization = 0.81 | pJ/Algorithmic-Compute =    2.943 | pJ/Compute =    5.255\n",
      "Utilization = 1.00 | pJ/Algorithmic-Compute =    1.649 | pJ/Compute =    1.649\n",
      "Utilization = 1.00 | pJ/Algorithmic-Compute =    1.649 | pJ/Compute =    1.649\n",
      "Utilization = 1.00 | pJ/Algorithmic-Compute =    1.566 | pJ/Compute =    1.566\n",
      "Utilization = 1.00 | pJ/Compute =    1.531\n",
      "Utilization = 1.00 | pJ/Algorithmic-Compute =    1.398 | pJ/Compute =    1.398\n",
      "Utilization = 1.00 | pJ/Algorithmic-Compute =    4.495 | pJ/Compute =    4.495\n",
      "Utilization = 1.00 | pJ/Algorithmic-Compute =    1.398 | pJ/Compute =    1.398\n",
      "Utilization = 1.00 | pJ/Algorithmic-Compute =    1.398 | pJ/Compute =    1.398\n",
      "Utilization = 1.00 | pJ/Algorithmic-Compute =    1.369 | pJ/Compute =    1.369\n",
      "Utilization = 1.00 | pJ/Compute =    1.832\n",
      "Utilization = 1.00 | pJ/Algorithmic-Compute =    1.149 | pJ/Compute =    2.297\n",
      "Utilization = 0.63 | pJ/Algorithmic-Compute =    1.269 | pJ/Compute =    5.769\n",
      "Utilization = 1.00 | pJ/Algorithmic-Compute =    1.149 | pJ/Compute =    2.297\n",
      "Utilization = 1.00 | pJ/Algorithmic-Compute =    1.149 | pJ/Compute =    2.297\n",
      "Utilization = 1.00 | pJ/Algorithmic-Compute =    0.817 | pJ/Compute =    1.633\n",
      "Utilization = 1.00 | pJ/Compute =    1.845\n",
      "Utilization = 1.00 | pJ/Algorithmic-Compute =    0.988 | pJ/Compute =    1.976\n",
      "Utilization = 0.50 | pJ/Algorithmic-Compute =    0.826 | pJ/Compute =    6.122\n",
      "Utilization = 1.00 | pJ/Algorithmic-Compute =    0.988 | pJ/Compute =    1.976\n",
      "Utilization = 1.00 | pJ/Algorithmic-Compute =    0.988 | pJ/Compute =    1.976\n",
      "Utilization = 1.00 | pJ/Algorithmic-Compute =    0.711 | pJ/Compute =    1.422\n",
      "Utilization = 1.00 | pJ/Compute =    1.781\n",
      "Utilization = 1.00 | pJ/Algorithmic-Compute =    1.189 | pJ/Compute =    2.378\n",
      "Utilization = 0.61 | pJ/Algorithmic-Compute =    1.802 | pJ/Compute =    6.436\n",
      "Utilization = 1.00 | pJ/Algorithmic-Compute =    1.189 | pJ/Compute =    2.378\n",
      "Utilization = 1.00 | pJ/Algorithmic-Compute =    1.189 | pJ/Compute =    2.378\n",
      "Utilization = 1.00 | pJ/Algorithmic-Compute =    1.106 | pJ/Compute =    2.212\n",
      "Utilization = 1.00 | pJ/Compute =    1.531\n",
      "Utilization = 1.00 | pJ/Algorithmic-Compute =    0.939 | pJ/Compute =    1.878\n",
      "Utilization = 0.68 | pJ/Algorithmic-Compute =    2.100 | pJ/Compute =    5.121\n",
      "Utilization = 1.00 | pJ/Algorithmic-Compute =    0.939 | pJ/Compute =    1.878\n",
      "Utilization = 1.00 | pJ/Algorithmic-Compute =    0.939 | pJ/Compute =    1.878\n",
      "Utilization = 1.00 | pJ/Algorithmic-Compute =    0.909 | pJ/Compute =    1.818\n",
      "Utilization = 0.52 | pJ/Algorithmic-Compute =    0.957 | pJ/Compute =    6.526\n",
      "Warning: DRAM: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Warning: SMEM: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Warning: LRF: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Utilization = 0.70 | pJ/Algorithmic-Compute =    0.995 | pJ/Compute =    2.986\n",
      "Utilization = 0.72 | pJ/Algorithmic-Compute =    0.987 | pJ/Compute =    2.961\n",
      "Utilization = 1.00 | pJ/Algorithmic-Compute =    0.655 | pJ/Compute =    1.965\n",
      "Utilization = 0.38 | pJ/Algorithmic-Compute =    0.596 | pJ/Compute =    6.623\n",
      "Warning: DRAM: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Warning: SMEM: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Warning: LRF: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Utilization = 0.70 | pJ/Algorithmic-Compute =    0.756 | pJ/Compute =    2.269\n",
      "Utilization = 0.72 | pJ/Algorithmic-Compute =    0.747 | pJ/Compute =    2.240\n",
      "Utilization = 1.00 | pJ/Algorithmic-Compute =    0.514 | pJ/Compute =    1.541\n",
      "Utilization = 0.53 | pJ/Algorithmic-Compute =    1.362 | pJ/Compute =    7.295\n",
      "Warning: DRAM: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Warning: SMEM: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Warning: LRF: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Utilization = 0.70 | pJ/Algorithmic-Compute =    0.980 | pJ/Compute =    2.941\n",
      "Utilization = 0.72 | pJ/Algorithmic-Compute =    0.972 | pJ/Compute =    2.916\n",
      "Utilization = 0.98 | pJ/Algorithmic-Compute =    0.889 | pJ/Compute =    2.667\n",
      "Utilization = 0.58 | pJ/Algorithmic-Compute =    1.511 | pJ/Compute =    5.529\n",
      "Warning: DRAM: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Warning: SMEM: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Warning: LRF: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Utilization = 0.70 | pJ/Algorithmic-Compute =    0.758 | pJ/Compute =    2.274\n",
      "Utilization = 0.72 | pJ/Algorithmic-Compute =    0.750 | pJ/Compute =    2.249\n",
      "Utilization = 0.76 | pJ/Algorithmic-Compute =    0.720 | pJ/Compute =    2.160\n",
      "Utilization = 0.44 | pJ/Algorithmic-Compute =    0.801 | pJ/Compute =    7.278\n",
      "Warning: DRAM: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Warning: SMEM: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Warning: LRF: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Utilization = 0.55 | pJ/Algorithmic-Compute =    0.913 | pJ/Compute =    3.650\n",
      "Warning: DRAM: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Warning: SMEM: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Warning: LRF: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Utilization = 0.55 | pJ/Algorithmic-Compute =    0.913 | pJ/Compute =    3.650\n",
      "Warning: DRAM: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Warning: SMEM: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Warning: LRF: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Utilization = 0.87 | pJ/Algorithmic-Compute =    0.581 | pJ/Compute =    2.322\n",
      "Utilization = 0.31 | pJ/Algorithmic-Compute =    0.483 | pJ/Compute =    7.158\n",
      "Warning: DRAM: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Warning: SMEM: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Warning: LRF: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Utilization = 0.55 | pJ/Algorithmic-Compute =    0.567 | pJ/Compute =    2.267\n",
      "Warning: DRAM: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Warning: SMEM: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Warning: LRF: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Utilization = 0.55 | pJ/Algorithmic-Compute =    0.567 | pJ/Compute =    2.267\n",
      "Warning: DRAM: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Warning: SMEM: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Warning: LRF: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Utilization = 1.00 | pJ/Algorithmic-Compute =    0.425 | pJ/Compute =    1.698\n",
      "Utilization = 0.51 | pJ/Algorithmic-Compute =    1.215 | pJ/Compute =    8.676\n",
      "Warning: DRAM: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Warning: SMEM: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Warning: LRF: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Utilization = 0.55 | pJ/Algorithmic-Compute =    0.953 | pJ/Compute =    3.812\n",
      "Warning: DRAM: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Warning: SMEM: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Warning: LRF: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Utilization = 0.55 | pJ/Algorithmic-Compute =    0.953 | pJ/Compute =    3.812\n",
      "Warning: DRAM: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Warning: SMEM: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Warning: LRF: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Utilization = 0.75 | pJ/Algorithmic-Compute =    0.870 | pJ/Compute =    3.480\n",
      "Utilization = 0.61 | pJ/Algorithmic-Compute =    1.259 | pJ/Compute =    6.141\n",
      "Warning: DRAM: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Warning: SMEM: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Warning: LRF: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Utilization = 0.55 | pJ/Algorithmic-Compute =    0.703 | pJ/Compute =    2.812\n",
      "Warning: DRAM: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Warning: SMEM: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Warning: LRF: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Utilization = 0.55 | pJ/Algorithmic-Compute =    0.703 | pJ/Compute =    2.812\n",
      "Warning: DRAM: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Warning: SMEM: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Warning: LRF: adjust representation format word bits from 3 to 4 to avoid storage fragmentation.\n",
      "Utilization = 0.58 | pJ/Algorithmic-Compute =    0.673 | pJ/Compute =    2.693\n",
      "sweep list path:  /home/nelliewu/research/workspace/sparse-modeling/micro22-sparseloop-artifact/workspace/2022.micro.artifact/evaluation_setups/fig15_stc_related_setup/scripts/../architecture_templates/HW_systems/design_points/MICRO22-STC-Case-Study.yaml\n",
      "[{'configurations': {'dataflow': 'FlexibleGH-Ms-constraints'},\n",
      "  'design_name': 'TC-RF2x-24-bandwidth'},\n",
      " {'configurations': {'dataflow': 'FlexibleGH-Ms-constraints',\n",
      "                     'representation': 'CP',\n",
      "                     'sparse_optimizations': 'RF-spatial-skipping'},\n",
      "  'design_name': 'STC-RF2x-24-bandwidth'},\n",
      " {'configurations': {'dataflow': 'DSTC-Os-constraints',\n",
      "                     'representation': 'B',\n",
      "                     'sparse_optimizations': 'LRF-PosTiling'},\n",
      "  'design_name': 'DSTC-RF2x-24-bandwidth'},\n",
      " {'configurations': {'dataflow': 'FlexibleGH-Ms-constraints',\n",
      "                     'representation': 'CP',\n",
      "                     'sparse_optimizations': 'RF-spatial-skipping'},\n",
      "  'design_name': 'STC_flexible-RF2x-24-bandwidth'},\n",
      " {'configurations': {'dataflow': 'FlexibleGH-Ms-constraints',\n",
      "                     'representation': 'RLE',\n",
      "                     'sparse_optimizations': 'RF-spatial-skipping'},\n",
      "  'design_name': 'STC_flexible-RF2x-24-bandwidth-RLE'},\n",
      " {'configurations': {'dataflow': 'FlexibleGH-Ms-constraints',\n",
      "                     'representation': 'RLE',\n",
      "                     'sparse_optimizations': 'RF-spatial-skipping-compressAB'},\n",
      "  'design_name': 'STC_flexible_dualCompress-RF2x-24-bandwidth-RLE'}]\n",
      "\n",
      "INFO: generating per layer results for:  1.0\n",
      "\n",
      "INFO: generating per layer results for:  0.5\n",
      "\n",
      "INFO: generating per layer results for:  0.3333\n",
      "\n",
      "INFO: generating per layer results for:  0.25\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "real\t1m30.611s\n",
      "user\t0m30.826s\n",
      "sys\t0m1.369s\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "cd ../evaluation_setups/fig15_stc_related_setup/scripts\n",
    "echo \"input specs at: ../evaluation_setups/fig15_stc_related_setup/\"\n",
    "time python3 sweep.py\n",
    "# To enable mapspace search, uncomment command below (note that the run will take much longer)\n",
    "# mapspace search might end up with a slightly different mapping due to runtime randomness\n",
    "# time python3 sweep.py --search_mapping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step2: Parse and generate plot\n",
    "\n",
    "1) run first cell to parse and generate figures\n",
    "\n",
    "2) run second cell to display the figures\n",
    "\n",
    "3) compare the generated figure to Fig.15 in the paper\n",
    "\n",
    "  ***Please note that we used the public 45nm data for energy modeling in this evaluation instead of the 65nm private PDK used in our paper's evaluations.*** As a result, the normalized cycles will match the figure presented in paper, but **the normalized energy will not be an exact match. However, the trends do stay the same for both technology nodes.**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dummping stats to  ../csv_results/resnet50_selected-normed-WD-1.0-TC-RF2x-24-bandwidth.csv\n",
      "Fig saved to  /home/nelliewu/research/workspace/sparse-modeling/micro22-sparseloop-artifact/workspace/2022.micro.artifact/evaluation_setups/fig15_stc_related_setup/outputs/fig_cycles.png\n",
      "Fig saved to  /home/nelliewu/research/workspace/sparse-modeling/micro22-sparseloop-artifact/workspace/2022.micro.artifact/evaluation_setups/fig15_stc_related_setup/outputs/fig_energy.png\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "cd ../evaluation_setups/fig15_stc_related_setup/scripts\n",
    "python3 parse_and_plot.py --raw ../outputs/resnet50_selected/*\n",
    "\n",
    "# If used mapspace search in the previous step\n",
    "# python3 parse_and_plot.py --raw ../outputs/resnet50_selected/* --stats_prefix timeloop-mapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import Video, Image, HTML, display\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "cycles_fig = \"../evaluation_setups/fig15_stc_related_setup/outputs/fig_cycles.png\"\n",
    "energy_fig = \"../evaluation_setups/fig15_stc_related_setup/outputs/fig_energy.png\"\n",
    "\n",
    "display(Image(cycles_fig))\n",
    "display(Image(energy_fig))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
